Using Context Based Machine Learning for Generation of Customized Driving Outputs

1. A computing platform comprising: at least one processor; a communication interface communicatively coupled to the at least one processor; and memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: receive, from a first computing device, first data corresponding to a first driver; receive, from a second computing device, second data corresponding to a second driver; input the first data and the second data into a machine learning model, wherein the machine learning model is trained using historical data corresponding to a plurality of drivers or passengers; classify the first data and the second data as corresponding to one or more of a trip context, a device interaction context, a physical condition context, or a personality context; apply a weight to one or more of the trip context, the device interaction context, the physical condition context, or the personality context; generate a customized driving output for the first driver based on one or more of the weighted trip context, the weighted device interaction context, the weighted physical condition context or the weighted personality context, wherein generating of the customized driving output for the first driver is based at least in part on the second data corresponding to the second driver; and cause a display of the first computing device to display the customized driving output.

2. The computing platform of claim 1, wherein training the machine learning model includes: classifying the historical data as corresponding to one or more of the trip context, the device interaction context, the physical condition context, or the personality context corresponding to a driver or passenger; and training the machine learning model corresponding to one or more of the trip context, the device interaction context, the physical condition context, or the personality context, using the corresponding classified historical data corresponding to the plurality of drivers or passengers.

3. The computing platform of claim 2, wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, further cause the computing platform to: identify that trip context data is not available for the first driver; identify that the trip context is available for a passenger of a vehicle operated by the first driver; identify that the first driver and the passenger travel together during a percentage of driving trips that exceeds a predetermined threshold; and based on identifying the percentage exceeds the predetermined threshold, use the trip context data for the passenger to train the machine learning model.

4. The computing platform of claim 1, wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, further cause the computing platform to: send, to the first computing device, the customized driving output for the first driver and one or more commands directing the first computing device to display the customized driving output for the first driver; and send the one or more commands directing the first computing device to display the customized driving output for the first driver causes the first computing device to display the customized driving output for the first driver.

5. The computing platform of claim 4, wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, further cause the computing platform to: receive, from the first computing device, an indication of a response to the customized driving output; and generate, using the machine learning model and based upon the response to the customized driving output, one or more contextual driving insights.

6. The computing platform of claim 5, wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, further cause the computing platform to: send, to an enterprise computing system, the one or more contextual driving insights, wherein the one or more contextual driving insights include a modification of a rate corresponding to the first driver.

7. The computing platform of claim 1, wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, further cause the computing platform to: update the machine learning model using at least one of the first data corresponding to the first driver or the second data corresponding to the second driver.

8. A method comprising: at a computing platform comprising at least one processor, a communication interface, and memory: receiving, from a first computing device, first data corresponding to a first driver; receiving, from a second computing device, second data corresponding to a second driver; inputting the first data corresponding to the first driver and the second data into a machine learning model, wherein the machine learning model is trained using historical data corresponding to a plurality of drivers or passengers; classifying the first data and the second data as corresponding to one or more of a trip context, a device interaction context, a physical condition context, or a personality context; applying a weight to one or more of the trip context, the device interaction context, the physical condition context, or the personality context; generating a customized driving output for the first driver based on one or more of the weighted trip context, the weighted device interaction context, the weighted physical condition context, or the weighted personality context, wherein generating of the customized driving output for the first driver is based at least in part on the second data corresponding to the second driver; and causing a display of the first computing device to display the customized driving output.

9. The method of claim 8, wherein training the machine learning model further comprises: classifying the historical data as corresponding to one or more of the trip context, the device interaction context, the physical condition context, or the personality context corresponding to a driver or passenger; and training the machine learning model corresponding to one or more of the trip context, the device interaction context, the physical condition context, or the personality context, using the corresponding classified historical data corresponding to the plurality of drivers or passengers.

10. The method of claim 9, the method further comprising: identifying that trip context data is not available for the first driver; identifying that the trip context is available for a passenger of a vehicle operated by the first driver; identifying that the first driver and the passenger travel together during a percentage of driving trips that exceeds a predetermined threshold; and based on identifying the percentage exceeds the predetermined threshold, using the trip context data for the passenger to train the machine learning model.

11. The method of claim 8, the method further comprising: sending, to the first computing device, the customized driving output for the first driver and one or more commands directing the first computing device to display the customized driving output for the first driver; and sending the one or more commands directing the first computing device to display the customized driving output for the first driver causes the first computing device to display the customized driving output for the first driver.

12. The method of claim 11, the method further comprising: receiving, from the first computing device, an indication of a response to the customized driving output; and generating, using the machine learning model and based upon the response to the customized driving output, one or more contextual driving insights.

13. The method of claim 12, the method further comprising: sending, to an enterprise computing system, the one or more contextual driving insights, wherein the one or more contextual driving insights include a modification of a rate corresponding to the first driver.

14. The method of claim 8, the method further comprising: updating the machine learning model using at least one of the first data corresponding to the first driver or the second data corresponding to the second driver.

15. One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, a communication interface, and memory, cause the computing platform to: receive, from a first computing device, first data corresponding to a first driver; receive, from a second computing device, second data corresponding to a second driver; input the first data corresponding to the first driver and the second data corresponding to the second driver into a machine learning model, wherein the machine learning model is trained using historical data corresponding to a plurality of drivers or passengers; classify the first data and the second data as corresponding to one or more of a trip context, a device interaction context, a physical condition context, or a personality context; apply a weight to one or more of the trip context, the device interaction context, the physical condition context, or the personality context; generate a customized driving output for the first driver based on one or more of the weighted trip context, the weighted device interaction context, the weighted physical condition context or the weighted personality context, wherein generating of the customized driving output for the first driver is based at least in part on the second data corresponding to the second driver; and cause a display of the first computing device to display the customized driving output.

16. The one or more non-transitory computer-readable media of claim 15, wherein training the machine learning model further includes: classifying the historical data as corresponding to one or more of the trip context, the device interaction context, the physical condition context, or the personality context corresponding to a driver or passenger; and training the machine learning model corresponding to one or more of the trip context, the device interaction context, the physical condition context, or the personality context, using the corresponding classified historical data corresponding to the plurality of drivers or passengers.

17. The one or more non-transitory computer-readable media of claim 16 storing instructions that, when executed by the computing platform, further cause the computing platform to: identify that trip context data is not available for the first driver; identify that the trip context is available for a passenger of a vehicle operated by the first driver; identify that the first driver and the passenger travel together during a percentage of driving trips that exceeds a predetermined threshold; and based on identifying the percentage exceeds the predetermined threshold, use the trip context data for the passenger to train the machine learning model.

18. The one or more non-transitory computer-readable media of claim 15 storing instructions that, when executed by the computing platform, further cause the computing platform to: send, to the first computing device, the customized driving output for the first driver and one or more commands directing the first computing device to display the customized driving output for the first driver; and send the one or more commands directing the first computing device to display the customized driving output for the first driver causes the first computing device to display the customized driving output for the first driver.

19. The one or more non-transitory computer-readable media of claim 18 storing instructions that, when executed by the computing platform, further cause the computing platform to: receive, from the first computing device, an indication of a response to the customized driving output; and generate, using the machine learning model and based upon the response to the customized driving output, one or more contextual driving insights.

20. The one or more non-transitory computer-readable media of claim 19 storing instructions that, when executed by the computing platform, further cause the computing platform to: send, to an enterprise computing system, the one or more contextual driving insights, wherein the one or more contextual driving insights include a modification of a rate corresponding to the first driver.